\begin{abstractlong}

Healthcare processes across the world have migrated from an approach where healthcare is provided in
generalist centres of care to one where the patient is referred between specialists who engage in
shared care.  The developers of recent Electronic Health Record (EHR) standards such as CEN EN13606
and HL7 version 3 aim to enable healthcare professionals in a shared care setting to deliver high
quality health data and share patient health information. A key innovation of these standards is a
progression from the traditional clinical information modelling method to the more detailed, context
specific and more tightly-constrained `two-level' based modelling approach. The `two-level'
modelling approach separates the fundamental information that is required to build an EHR system
from the clinical information that health experts wish to exchange. These models consist firstly of
a `Reference Model', that describes common and generic information such as document structures,
basic data types and abstract concepts of an EHR system. The second level, focusing on how to create
exchangeable information for different clinical scenarios, consists of a dynamic platform to allow
healthcare experts to design the clinical information or messages that are exchanged. Examples of
this level include openEHR Archetypes and Templates. Recent research effort has also focused on the
complementary approach of clinical terminology systems; standard coding systems to encode clinical
data in an unambiguous format so that the clinical meanings can be interpreted correctly throughout
the healthcare system.

The integration of clinical terminology with the data in an EHR is essential to achieve semantic
interoperability in e-Health. For complex systems that implement two-level model based EHRs, it
requires much effort from domain experts to work with both archetypes and a large, comprehensive
terminology system such as SNOMED-CT.

This thesis investigates a mechanism that allows standard codes from clinical terminologies to be
linked with data specified by archetypes, and develops a \emph{Terminological Shadow} approach to enhance
this integration. This thesis demonstrates that the construction of artefacts, known as
\emph{Terminological Shadows} of archetypes, will improve the archetype modelling process and provide a
guide for further development of archetypes to cover and convey more semantically accurate clinical
meanings.



\end{abstractlong}
